class_docstr: This class tests the ensemble classifier.
class_docstr_tok: ["This","class","tests","the","ensemble","classifier","."]
code: def test_predict(self):
      predictions = self.ensemble.predict(self.x_test_mnist, raw=False)
      self.assertTrue(predictions.shape, (self.n_test, 10))
  
      expected_predictions_1 = np.asarray(
          [
              0.12109935,
              0.0498215,
              0.0993958,
              0.06410097,
              0.11366927,
              0.04645343,
              0.06419807,
              0.30685693,
              0.07616713,
              0.05823759,
          ]
      )
      np.testing.assert_array_almost_equal(
          predictions[0, :], expected_predictions_1, decimal=4
      )
  
      predictions_raw = self.ensemble.predict(
          self.x_test_mnist, raw=True
      )
      self.assertEqual(predictions_raw.shape, (2, self.n_test, 10))
  
      expected_predictions_2 = np.asarray(
          [
              0.06054967,
              0.02491075,
              0.0496979,
              0.03205048,
              0.05683463,
              0.02322672,
              0.03209903,
              0.15342847,
              0.03808356,
              0.02911879,
          ]
      )
      np.testing.assert_array_almost_equal(
          predictions_raw[0, 0, :], expected_predictions_2, decimal=4
      )
  
file_path: adversarial-robustness-toolbox/tests/estimators/classification/test_ensemble.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import keras.backend
  import numpy
  from art.estimators.classification.ensemble import EnsembleClassifier
  from tests.utils import TestBase, get_image_classifier_kr
label_desc: Use image node_id 6 for calling the TestEnsembleClassifier obj's underlying member method code with example usage: obj.test_predict() without return types